class_docstr: Computes the forward decoding in a linear-chain CRF.
docstr: Build the CrfDecodeForwardRnnCell.
  
  Args:
    inputs: A [batch_size, num_tags] matrix of unary potentials.
    state: A [batch_size, num_tags] matrix containing the previous step's
          score values.
  
  Returns:
    backpointers: A [batch_size, num_tags] matrix of backpointers.
    new_state: A [batch_size, num_tags] matrix of new score values.
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code: def call(self, inputs, state):
      """Build the CrfDecodeForwardRnnCell.
  
      Args:
        inputs: A [batch_size, num_tags] matrix of unary potentials.
        state: A [batch_size, num_tags] matrix containing the previous step's
              score values.
  
      Returns:
        backpointers: A [batch_size, num_tags] matrix of backpointers.
        new_state: A [batch_size, num_tags] matrix of new score values.
      """
      state = tf.expand_dims(state[0], 2)
      transition_scores = state + tf.cast(
          self._transition_params, self._compute_dtype
      )
      new_state = inputs + tf.reduce_max(transition_scores, [1])
      backpointers = tf.argmax(transition_scores, 1)
      backpointers = tf.cast(backpointers, dtype=tf.int32)
      return backpointers, new_state
  
imports: import warnings
  import numpy
  import tensorflow
  from tensorflow_addons.rnn.abstract_rnn_cell import AbstractRNNCell
  from tensorflow_addons.utils.types import TensorLike
  from typeguard import typechecked
  from typing import Optional, Tuple
label_desc: Use image node_id 5 for calling the CrfDecodeForwardRnnCell obj's underlying member method code with example usage: obj.call(inputs, state) and returns: backpointers,  new_state